Method and apparatus for deciding cause of abnormality in plasma processing apparatus

ABSTRACT

Analysis data constituted by a plurality of parameters is acquired on the basis of detection values obtained in processes for an object to be processed from a detector arranged in a plasma processing apparatus. With respect to parameters of analysis data decided as abnormal data, as a degree of influence on abnormality, a contribution to, e.g., a residual score is calculated (degree-of-influence calculating step). Contributions of the parameters are set at 0 or a value close to 0 in a descending order of contribution of the parameters to sequentially calculate residual scores, and, when the residual scores are not more than a predetermined value, the parameters having the contributions which are set at 0 or a value close to 0 until now as parameters which cause abnormality (cause-of-abnormality deciding step).

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates to a method and apparatus for deciding a cause ofabnormality in a plasma processing apparatus.

2. Description of the Related Art

In the steps in manufacturing a semiconductor, manufacturing apparatusesand inspection apparatuses of various types are used. For example, aplasma processing apparatus performs an etching process, a film formingprocess, and the like to an object to be processed such as asemiconductor wafer or a glass substrate by generating a plasma in aprocessing chamber. These processing apparatuses have a large number ofparameters to control or monitor the operation states of the processingapparatuses. The processing apparatuses the operation states to make itpossible to various processes under optimum conditions. As theparameters, temperatures of an upper electrode, a lower electrode, andthe like arranged in a processing chamber.

When processes are performed by the above plasma processing apparatus,the plasma processing apparatus is controlled such that the processescan be always optimally performed while monitoring the parameters bydetectors, respectively. At this time, the number of parameters comes toseveral tens. For this reason, when an abnormality is recognized in anoperation state, a cause of abnormality is very difficult to be foundout.

For example, in Japanese Patent Laid-open Publication No. 11-87323, thefollowing technique is disclosed. That is, a plurality of processparameters of a semiconductor wafer processing system are analyzed tocorrelate the parameters to each other as analysis data, therebydetecting changes of process characteristics and system characteristics.

The following method is also known. That is, the plurality of parametersare held together into a smaller number of statistical data as analysisdata by using a method of analyzing a principal component which is oneof multivariate analyses. Operation states of a processing apparatus ismonitored on the basis of the small number of statistical data toevaluate the operation states.

In such a conventional method, for example, analysis data is subjectedto, e.g., a principal component analysis on the basis of the statisticalanalysis result obtained by the principal component analysis tocalculate a residual score. The residual score is a predetermined valueor more, it is decided that the analysis data is abnormal.

However, by the above methods, it can be decided whether the analysisdata is abnormal or not. Of the parameters constituting the analysisdata, a parameter which causes an abnormality cannot be specified.

With respect to this point, when degrees of contribution of theparameters in the analysis data are calculated to a residual score, aspecific parameter which causes an abnormality can be known to someextent. More specifically, since a parameter having a high contributionlargely contributes to a residual score of a part decided as an abnormalpart, a parameter having a high contribution is abnormal with highprobability.

However, a specific parameter cannot be decided as an abnormal parameterdue to the contribution of a residual score. For this reason, aparameter which causes an abnormality cannot be appropriately specified.

SUMMARY OF THE INVENTION

Therefore, the present invention has been made in consideration of theabove problems, and has as its object to provide a method and apparatusfor deciding a cause of abnormality in a plasma processing apparatus tomake it possible to appropriately specify a parameter of analysis datawhich causes an abnormality.

In order to solve the above object, according to the first aspect of thepresent invention, there is provided a method of deciding a cause ofabnormality in a plasma processing apparatus which performs a plasmaprocessing to an object to be processed in a processing chamber,including: the analysis data acquiring step of acquiring analysis dataconstituted by a plurality of parameters on the basis of detectionvalues obtained in processes for the object from a detector arranged inthe plasma processing apparatus; the abnormality deciding step ofanalyzing the acquired analysis data to decide whether the analysis datais abnormal or not; the degree-of-influence calculating step ofcalculating degrees of influence on the abnormality of each parameter ofthe analysis data decided as abnormal data; and the cause-of-abnormalitydeciding step of deciding whether the analysis data is abnormal or notafter removing the influences on the abnormality from the parametersequentially in the descending order of the degrees of influence of theparameters, and then deciding the parameters from which the influenceson the abnormality are removed up to now as parameters which cause theabnormality when it is decided that the analysis data is normal.

In order to solve the above problem, according to the second aspect ofthe present invention, there is provided an apparatus for deciding acause of abnormality in a plasma processing apparatus which performs aplasma processing to an object to be processed in a processing chamber,including: analysis data acquiring means for acquiring analysis dataconstituted by a plurality of parameters on the basis of detectionvalues obtained in processes for the object from a detector arranged inthe plasma processing apparatus; abnormality deciding means foranalyzing the acquired analysis data to decide whether the analysis datais abnormal or not; degree-of-influence calculating means forcalculating degrees of influence on the abnormality of each parameter ofthe analysis data decided as abnormal data; and cause-of-abnormalitydeciding means for deciding whether the analysis data is abnormal or notafter removing the influences on the abnormality from the parametersequentially in the descending order of the degrees of influence of theparameters, and then deciding the parameters from which the influenceson the abnormality are removed up to now as parameters which cause theabnormality when it is decided that the analysis data is normal.

In the first and second aspects of the present invention, since aparameter can be specified depending on a degree of influence on anabnormality can be specified, a parameter which causes an abnormalitycan be appropriately decided. For this reason, since repair ormaintenance required to decide the analysis data as normal data can beappropriately performed, the repair or the maintenance can be madeefficient.

In the abnormality decision in the method and apparatus, principalcomponent analysis is performed to the acquired analysis data tocalculate a residual score, and analysis data the residual score ofwhich exceeds a predetermined value is decided as abnormal data. In thecalculation of degrees of influence, degrees of contribution to theresidual score are calculated as the degrees of influence on theabnormality with respect to parameters of the analysis data decided asabnormal data. In the decision of a cause of abnormality, residualscores are sequentially calculated in the descending order of thedegrees of contribution of the parameters such that the degrees ofcontribution of the parameters are set to be 0 or a value close to 0,and when the residual scores are the predetermined value or less, theparameters the degrees of contribution of which are set to be 0 or avalue close to 0 may be decided as parameters which cause theabnormality. According to this, since a parameter can be specifieddepending on a residual score obtained by a principal componentanalysis, the parameter which causes an abnormality can be appropriatelydecided.

In the decision of a cause of abnormality in the method and apparatus,in order to set the contribution of a specific parameter to be 0 or avalue close to 0, a correlation between the specific parameter andanother parameter is calculated by a multivariate analysis, e.g., apartial least squares method, a predicted value of the specificparameter is calculated on the basis of the correlation, and the valueof the specific parameter may be replaced with the predicted value. Inthis manner, even though a new special computing method is not made, thecontribution of the specific parameter can be set to be 0 or a valueclose to 0.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram showing a plasma processing apparatusaccording to an embodiment of the present invention.

FIG. 2 is a block diagram showing an example of a multivariate analysisunit in the embodiment.

FIG. 3 is a graph of a residual score Q obtained when a model is formedby performing a principal component analysis to analysis data in theembodiment.

FIG. 4 is a graph of a contribution to the residual score Q with respectto analysis data in a wafer which is decided to be abnormal in theembodiment.

FIG. 5 is a graph of a contribution to the residual score Q when aparameter C1_U is simply replaced with an average value with respect tothe analysis data in the wafer which is decided to be abnormal in theembodiment.

FIG. 6 is a graph of a contribution to the residual score Q when adegree of influence of the parameter C1_U having the highestcontribution is removed with respect to the analysis data in the waferwhich is decided to be abnormal in the embodiment.

FIG. 7 is a graph of a contribution to the residual score Q when adegree of influence of a parameter C2_L having the next highestcontribution is removed with respect to the analysis data in the waferwhich is decided to be abnormal in the embodiment.

FIG. 8 is a graph of the residual score Q when the degree of influenceof the parameter C1_U having the highest contribution is removed in theembodiment.

FIG. 9 is a graph of the residual score Q when the degree of influenceof the parameter C2_L having the next highest contribution is removed inthe embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Preferred embodiments of an apparatus according to the present inventionwill be described below with reference to the accompanying drawings. Thesame reference numerals as in the specification and the drawings denoteconstituent elements substantially having the same functions in thespecification and the drawings.

(Plasma Processing Apparatus)

A plasma processing apparatus 100 according to the first embodiment willbe described below. The plasma processing apparatus 100, as shown inFIG. 1, comprises an aluminum processing chamber 101, an aluminumsupport member 103 that supports a lower electrode 102 arranged in theprocessing chamber 101 through an insulator 102A and that can bevertically moved, and a shower head (to be also referred to as an “upperelectrode” hereinafter) 104 that is arranged, supplies a process gas,and also serves as an upper electrode. The shower head 104 insulatedfrom the processing chamber 101 through an insulator 104C.

A high-frequency power supply 104E is connected to the shower head 104through a rectifier 104D. The first high-frequency power supply 104E hasa frequency falling within the range of, e.g., 50 to 150 MHz. In thismanner, when a high-frequency power is applied, a high-density plasmacan be formed with a preferable dissociation state in the processingchamber 101 to make it possible to perform a plasma processing at apressure lower than a conventional pressure. The pressure of the firsthigh-frequency power supply 104E preferably falls within the range of 50to 80 MHz. Typically, a frequency of 60 MHz (not shown) or anapproximate frequency is employed.

In the rectifier 104D on the shower head 104 side, a measuring unit 104b which measures a high-frequency (RF) voltage Vpp on the shower head104 side (output side of the high-frequency voltage) is arranged. Morespecifically, for example, two variable capacitors C1_U and C2_U, acapacitor C, and a coil L are incorporated to achieve impedance matchingthrough variable capacitors C1_U and C2_U.

The rectifier 104D comprises a voltmeter 104 a. A voltage Vdc between asupply line (electric wire) for a first high-frequency power and theground of the plasma processing apparatus 100 can be measured by thevoltmeter 104 a.

A first high-frequency power P from the first high-frequency powersupply 104E is measured by the voltmeter 104 d connected to the upperelectrode 104 side (output side of the high-frequency power) of therectifier 104D.

A detection window 120 a is arranged on the side wall of the processingchamber 101, and a spectroscope (to be referred to as an “opticalmeasuring unit” hereinafter) 120 that detects plasma emission in theprocessing chamber 101 through the detection window 120 a is arrangedoutside the side wall of the processing chamber 101. An emissionspectrum intensity of a wavelength detected from the spectroscope 120 isused as optical data.

In the processing chamber 101, a chamber 101A having an uppersmall-diameter part is formed as an upper part, and a chamber 101Bhaving a lower large-diameter part is formed as a lower part. Aninlet/outlet port which is used to carry the wafer W out of or into alower chamber 101B is formed in the upper part of the lower chamber101B, and a gate valve 106 is attached to the outlet/inlet port.

A second high-frequency power supply 107 is connected to the lowerelectrode 102 through an electric measuring unit 107C (for example, a VIprobe), a rectifier 107A, and a voltmeter 107B. The secondhigh-frequency power supply 107 has a frequency falling within the rangeof several hundred kHz to ten and several MHz. When the power having thefrequency falling within such a range is applied, ionic action can beappropriately achieved without damaging the wafer W serving as an objectto be processed. The frequency of the second high-frequency power supply107, an illustrated frequency of 2 MHz is typically employed.

The rectifier 107A on the lower electrode 102 side is constituted likethe voltmeter 104D on the upper electrode 104 side. More specifically,in the rectifier 107A, a measuring unit 107 b that measures thehigh-frequency (RF) voltage Vpp on the lower electrode 102 side (outputside of a high-frequency voltage) is arranged. The rectifier 107A, morespecifically, incorporates, e.g., two variable capacitors C1_L and C2_L,a capacitor C, and a coil L to achieve impedance matching through thevariable capacitors C1_L and C2_L.

The rectifier 107D comprises a voltmeter 107 a. A voltage Vdc between asupply line (electric wire) for a second high-frequency power and theground of the plasma processing apparatus 100 can be measured by thevoltmeter 107 a.

A second high-frequency power P from the second high-frequency powersupply 107 is measured by the voltmeter 107B connected to the lowerelectrode 102 side (output side of the high-frequency power) of therectifier 107A.

High-frequency voltages (V), high-frequency currents (I), high-frequencyphases (P), and impedances (Z) of a fundamental wave (for example, atraveling wave and a reflected wave of a high-frequency power) and aharmonic wave based on a plasma generated in the upper chamber 101A canbe detected as electric data by the high-frequency power P applied tothe lower electrode 102 through the electric measuring unit (for examplea VI probe) 107C. In this embodiment, the electric data such as thevoltages (V), the currents (I), the phases (P), and the impedances (Z)of the fundamental wave and the harmonic wave is used as operation datain prediction.

On the upper surface of the lower electrode 102, an electrostatic chuck108 is arranged. A DC power supply 109 is connected to an electrodeplate 108A of the electrostatic chuck 108. According to theelectrostatic chuck 108, a high voltage is applied from the DC powersupply 109 to the electrode plate 108A in a high vacuum state to make itpossible to electrostatically adsorb the wafer W. A volt/ampere meter109 a which detects an applied current and an applied voltage of theelectrostatic chuck 108 is connected between the electrode plate 108Aand the DC power supply 109 of the electrostatic chuck 108.

A focus ring 110 a is arranged on the periphery of the lower electrode102 to collect a plasma generated in the upper chamber 101A on the waferW. A discharge ring 111 attached to the upper portion of the supportmember 103 is arranged below the focus ring 110 a. A plurality of holesare formed on the entire periphery of the discharge ring 111 at equalintervals in the circumferential direction, and the gas in the upperchamber 101A is discharged to the lower chamber 101B through the holes.

The support member 103 can be vertically moved between the upper chamber101A and the lower chamber 101B through a ball screw mechanism 112 and abellows 113. Therefore, when the wafer W is supplied onto the lowerelectrode 102, the lower electrode 102 moves down to the lower chamber101B through the support member 103, and the gate valve 106 is releasedto supply the wafer W onto the lower electrode 102 through a carrymechanism (not shown).

A coolant flow path 103A connected to a coolant pipe 114 is formed inthe support member 103. A coolant is circulated in the coolant flow path103A through the coolant pipe 114 to control the temperature of thewafer W to a predetermined temperature.

Gas flow paths 103B are formed in the support member 103, the insulator102A, the lower electrode 102, and the electrostatic chuck 108,respectively. For example, an He gas is supplied as a back-side gas froma gas feeding mechanism 115 into a gap between the electrostatic chuck108 and the wafer W through a gas pipe 115A to increase the heatconductivity between the electrostatic chuck 108 and the wafer W throughthe He gas. The pressure of the back-side gas is detected by a pressuresensor (not shown), and the detection value is displayed on a pressuremeter 115B. Reference numeral 116 denotes a bellows cover. A mass flowcontroller (not shown) is arranged on the gas feeding mechanism 115. Themass flow controller can detect a gas flow rate of the back-side gas.

A gas feeding unit 104A is formed on the upper surface of the upperelectrode 104. A process gas supply system 118 is connected to the gasfeeding unit 104A through a pipe 117. The process gas supply system 118is formed such that a process gas supplier 118C is connected to the pipe117 through a valve 118A and a mass flow controller 118B.

From the process gas supplier 118C, a process gas for plasma etching issupplied. FIG. 1 shows only one process gas supply system 118constituted by the process gas supplier 118C and the like. However, theplurality of process gas supply system may be arranged. In this case,for example, the process gas supply systems are designed such that theflow rates of an NH₃ gas, an Ar gas, and the like are independentlycontrolled to be supplied into the processing chamber 101.

A plurality of holes 104B are uniformly formed on the entire lowersurface of the upper electrode 104. For example, an NH₃ gas is suppliedas a process gas from the upper electrode 104 into the upper chamber101A through the holes 104B.

In FIG. 1, reference numeral 101C denotes a discharge pipe, andreference numeral 119 denotes a discharge system constituted by a vacuumpump and the like connected to the discharge pipe 101C. An APC (AutoPressure Controller) valve 101D is arranged in the discharge pipe 101C,so that the divergence of the APC valve is automatically regulateddepending on a gas pressure in the processing chamber 101.

(Multivariate Analysis Means)

A multivariate analysis means held by the plasma processing apparatus100 in the embodiment will be described below. A multivariate analysismeans 200 also functions as an apparatus for deciding a cause ofabnormality of the plasma processing apparatus. The multivariateanalysis means 200, for example, as shown in FIG. 2, comprises amultivariate analysis program storing means 201 that stores amultivariate analysis program for a principal component analysis or apartial least squares method, an electric signal sampling means 202 thatintermittently samples signals from the electric measuring unit 107C,the spectroscope 120, and a parameter measuring unit 121, an opticalsignal sampling means 203, and a parameter signal sampling means 204.The data sampled by the sampling means 202, 203, and 204 serve asdetection values from the detectors, respectively.

The parameter measuring unit 121 is a measuring unit that measures thecontrol parameters described above. When a multivariate analysis isactually performed, all the data may not be used. The multivariateanalysis is performed by using at least one selected from the data fromthe electric measuring unit 107C, the spectroscope 120, and theparameter measuring unit 121. Therefore, the data from all the measuringunits may be used, or data from only the electric measuring unit 107C,the spectroscope 120, or the parameter measuring unit 121. Desired datafrom the electric measuring unit 107C, the spectroscope 120, and theparameter measuring unit 121 may be combined to each other.

The plasma processing apparatus 100 comprises an analysis result storingmeans 205 that stores a result of a multivariate analysis such as amodel formed by a multivariate analysis, a calculation means 206 thatdetects (diagnoses) an abnormal value of a predetermined parameter orcalculates a predicted value on the basis of the analysis result, aprediction/diagnosis/control means 207 that performs prediction,diagnosis, and control on the basis of a calculation signal from thecalculation means 206, and a data storing means 211 that stores analysisdata or the like.

A control device 122 that controls the plasma processing apparatus 100,an alarm device 123, and a display device 124 are connected to themultivariate analysis means 200. The control device 122 continues orinterrupts processing for the wafer W on the basis of a signal from, forexample, the prediction/diagnosis/control means 207. The alarm device123 and the display device 124 notifies the multivariate analysis means200 of abnormalities of a control parameter and/or an apparatus stateparameter on the basis of a signal from the prediction/diagnosis/controlmeans 207 as described below.

The calculation means 206 comprises an analysis means 212. The analysismeans 212, for example, performs a multivariate analysis such as a PCA(Principal Component Analysis) or a PLS method (Partial Least Squaresmethod). The analysis means 212 calculates a residual score obtained bythe principal analysis (as will be described later) or calculatesdegrees of contribution to the residual score with respect to theparameters of the analysis data.

(Abnormality Decision of Analysis Data)

A method of performing an abnormality decision by the analysis means 212will be described below. The analysis means 212 performs a multivariateanalysis such as a principal component analysis to the analysis data tocalculate a residual score Q, and an abnormality decision of theanalysis data is performed on the basis of the residual score Q.

More specifically, an etching process is performed to wafers located in,e.g., a predetermined zone serving as a reference in advance, detectionvalues detected by the detectors at this time, i.e., detection valuessuch as the high-frequency (RF) voltages Vpp are sequentially detectedwith respect to the wafers. Analysis data is acquired from the obtaineddetection values and values obtained by performing a predeterminedcalculation to the detection values, and is stored in, e.g., the datastoring means 211 (analysis data acquiring step and analysis dataacquiring means). When K detection values x are present for each of Nwafers, a matrix X including the analysis data is expressed by equation(1-1):

$\begin{matrix}{X = \begin{bmatrix}x_{11} & x_{12} & \ldots & x_{1K} \\x_{21} & x_{22} & \ldots & x_{2K} \\\vdots & \vdots & \vdots & \vdots \\x_{N1} & x_{N2} & \ldots & x_{NK}\end{bmatrix}} & \left( {1\text{-}1} \right)\end{matrix}$

After an average value, a maximum value, a minimum value, and a variancevalue are calculated on the basis of the detection values in thecalculation means 206, a principal component analysis is performed tothe analysis data by using a variance-covariance matrix based on thecalculated values to calculate characteristic numbers and characteristicvectors thereof.

The characteristic numbers express the sizes of a variances of theanalysis data, and are defined as a first principal component, a secondprincipal component, . . . , the ath principal component in a descendingorder of the characteristic numbers. Each characteristic number has acharacteristic vector which belongs to the characteristic number. Ingeneral, a high-order principal component has a high contribution toevaluation of data, and the utility value of the principal componentdecreases.

For example, K detection values are employed for each of N wafers, theath principal component corresponding to the ath characteristic numberof the nth wafer is expressed by equation (1-2):t _(na) =x _(n1) p _(1a) +x _(n2) p _(2a) + . . . +x _(nk) p_(Ka)  (1-2)

A vector t_(a) and a matrix T_(a) of the ath principal score areexpressed by equation (1-3), and a characteristic vector p_(a) and amatrix P_(a) of the ath principal component are defined by equation(1-4). The vector t_(a) of the ath principal component score isexpressed by equation (1-5) using the matrix X and the characteristicvector p_(a). When vectors t₁ to t_(k) of principal component scores andcharacteristic vectors p₁ to p_(k) are used, the matrix X is expressedby equation (1-6). In equation (1-6), P_(k) ^(T) means a transposedmatrix of P_(k).

$\begin{matrix}{{t_{a} = \begin{bmatrix}t_{1a} \\t_{2a} \\\vdots \\t_{Na}\end{bmatrix}},{T_{a} = {\begin{bmatrix}t_{11} & t_{12} & \ldots & t_{1a} \\t_{21} & t_{22} & \ldots & t_{2a} \\\vdots & \vdots & \vdots & \vdots \\t_{N1} & t_{N2} & \ldots & t_{Na}\end{bmatrix}\mspace{14mu}\left( {{a = 1},2,\ldots\mspace{11mu},K} \right)}}} & \left( {1\text{-}3} \right) \\{{p_{a} = \begin{bmatrix}p_{a1} \\p_{a2} \\\vdots \\p_{aK}\end{bmatrix}},{P_{a} = {\left\lbrack {p_{1},p_{2},\ldots\;,p_{a}} \right\rbrack\mspace{11mu}\left( {{a = 1},2,\ldots\;,K} \right)}}} & \left( {1\text{-}4} \right) \\{t_{a} = {Xp}_{a}} & \left( {1\text{-}5} \right) \\{X = {{T_{K}P_{K}^{T}} = {{t_{1}p_{1}^{T}} + {t_{2}p_{2}^{T}} + \ldots + {t_{K}p_{K}^{T}}}}} & \left( {1\text{-}6} \right)\end{matrix}$

In addition, a residual matrix E (components of in rows correspond todetection values of the detectors, and components in columns correspondto the numbers of wafers) defined by equation (1-7) obtained by summingup the (a+1)th or higher high-order principal components having lowdegrees of contribution is formed. When the residual matrix E is appliedto equation (1-6), equation (1-6) is expressed by equation (1-8). Aresidual score Q_(n) of the residual matrix E is defined by (1-10) usinga row vector e_(n) defined by equation (1-9). In equation (1-10), Q_(n)means the nth wafer.

$\begin{matrix}\begin{matrix}{E = {{t_{a + 1}p_{a + 1}^{T}} + \ldots + {t_{K}p_{K}^{T}}}} \\{= \begin{bmatrix}e_{11} & e_{12} & \ldots & e_{1K} \\e_{21} & e_{22} & \ldots & e_{2K} \\\vdots & \vdots & \vdots & \vdots \\e_{N1} & e_{N2} & \ldots & e_{NK}\end{bmatrix}}\end{matrix} & \left( {1\text{-}7} \right) \\{X = {{{T_{a}P_{a}^{T}} + E} = {{t_{1}p_{1}^{T}} + {t_{2}p_{2}^{T}} + \ldots + {t_{a}p_{a}^{T}} + E}}} & \left( {1\text{-}8} \right) \\{e_{n} = \left\lbrack {e_{n1},e_{n2},\ldots\;,e_{nK}} \right\rbrack} & \left( {1\text{-}9} \right) \\{Q_{n} = {e_{n}e_{n}^{T}}} & \left( {1\text{-}10} \right)\end{matrix}$

The residual score Q_(n) means a residual (error) of the nth wafer, andis defined by the equation (1-10). The residual score Q_(n) is expressedby a product of the row vector e_(n) and the transposed vector e_(n)^(T) thereof. and is a sum of squares of residuals. The residual scoreQ_(n) can be reliably calculated as a residual without canceling a pluscomponent and a minus component. In this embodiment, an operation stateis multilaterally decided and evaluated by calculating the residualscore Q.

More specifically, when the residual score Q_(n) of a certain wafer isdifferent from a residual score Q_(o) of a sampling wafer, when thecomponents of the row vector e_(n) are observed, it is understood thatany detection value of the wafer has a large error in processing of thewafer. Therefore, a cause of abnormality can be specified.

In the rows (in the same wafer) of the residual matrix E, when analysisdata having an error of residuals of the detectors is observed, aspecific detection value having abnormality can be correctly confirmedin the wafer.

(Operation of Plasma Processing Apparatus)

An operation of the plasma processing apparatus 100 will be describedbelow. In this embodiment, the plasma processing apparatus 100 acquiresdetection values from measuring units as analysis data each time plasmaprocessing of one wafer is performed. These analysis data are stored in,e.g., The data storing means 211.

When the operation of the plasma processing apparatus 100 is started,the support member 103 moves downward to the lower chamber 101B of theprocessing chamber 101 through the ball screw mechanism 112, and thesupport member 103 carries a wafer W from the inlet/output port openedby the gate valve 106 to place the wafer W on the lower electrode 102.After the wafer W is carried, the gate valve 106 is closed, and thedischarge system 119 operates to keep a state in the processing chamber101 at a predetermined degree of vacuum. At this time, an He gas issupplied from the gas feeding mechanism 115 as a back gas at a centerpressure of 20 Torr and an edge pressure of 40 Torr to increase heatconductivity between the wafer W and the lower electrode 102, morespecifically, the electrostatic chuck 108 and the wafer W, so thatcooling efficiency of the wafer W is improved. The temperature of theupper electrode is set at 60° C., the temperature of the lower electrodeis set at 20° C., and the temperature of the side wall is set at 60° C.

On the other hand, a process gas is supplied from the process gas supplysystem 118. More specifically, an NH₃ gas is supplied at a predeterminedflow rate. A pressure in the processing chamber 101 at this time is,e.g., 175 mT. In this state, a high-frequency power having 60 MHz and2000 W is applied from the high-frequency power supply 104E to the upperelectrode 104, and a high-frequency power of 2 MHz and 1800 W is appliedfrom the second high-frequency power supply 107 to the lower electrode102. In this manner, a plasma of the process gas is generated to performetching process of a non-etching layer on the wafer W constituted by,e.g., a silicon substrate. Upon completion of the etching process, anoperation opposite to the carry-in operation is performed to convey theprocessed wafer W out of the processing chamber 101. The same processesas described above are repeated to the subsequent wafers W, apredetermined number of wafers W are processed, and the series ofprocesses are ended.

While the processes of the wafers W are performed, as analysis data, forexample, the temperature and the like of the upper electrode 104, thewall surface of the upper chamber 101A of the processing chamber 101,and the lower electrode 102 are intermittently detected from themeasuring units. The detection signals are sequentially input to themultivariate analysis means 200 through an A/D converter and stored inthe data storing means 211 arranged in the multivariate analysis means200.

In this embodiment, as the analysis data, the following data are used:

-   APC: divergence of APC valve 101D-   C1_L: position of variable capacitor C1_L of rectifier 107A-   C1_U: position of variable capacitor C1_U of rectifier 104D-   C2_L: position of variable capacitor C2_L of rectifier 107A-   C2_U: position of variable capacitor C2_U of rectifier 104D-   EPD_A: light intensity of A wavelength (for example 387.5 nm)-   EPD_B: light intensity of B wavelength (for example 260 nm)-   EPD_DEF: derivative value of A wavelength/B wavelength-   EPD_RAT: value of A wavelength/B wavelength-   EAC_CIE: applied current of electrostatic chuck detected by the    volt/ampere meter 109 a-   T_L: temperature of lower electrode 102-   REF_L: reflected wave of high-frequency power applied to lower    electrode 102-   REF_U: reflected wave of high-frequency power applied to upper    electrode 104-   RF_Vpp: high-frequency voltage (RF voltage) Vpp on output side of    rectifier 107A-   T_U: temperature of upper electrode 104-   T_W: temperature of side wall of processing chamber.

The light intensities of the A wavelength (for example, 387.5 nm) andthe B wavelength (for example, 260 nm) are calculated by measurementperformed by the spectroscope 120. The reflected wave of thehigh-frequency power applied to the lower electrode 102 is measured by areflected wave measuring unit (not shown) arranged in the rectifier107A. The reflected wave of the high-frequency power applied to theupper electrode 104 is measured by a reflected wave measuring unit (notshown) arrange in the rectifier 104D.

In this manner, a principal component analysis is performed to theanalysis data obtained each time the wafers W are processes to calculatea residual score Q. When the residual score Q exceeds a predeterminedvalue, the data is decided as abnormal data.

(Decision of Cause of Abnormality)

A method of deciding a specific parameter having abnormality in theanalysis data which is decided as abnormal data in the decision ofabnormality, for example, analysis data having a residual score Q whichis exceeds a predetermined value will be described below.

In this case, for example, the 1st to 125th wafers are subjected to aprincipal component analysis as sample wafers to form models, andresidual scores Q of the 1st to 250th wafers are calculated are shown inFIG. 3. According to FIG. 3, the residual scores Q of the 126th to 150thwafers are largely shifted from the residual scores Q of the otherwafers W, and the wafers can be decided as abnormal wafers.

In this manner, a cause obtained when the analysis data is decided asabnormal data can be specified by a contribution to the residual scoresQ of the parameters of the analysis data contributed such that theresidual scores Q exceeds the predetermined value. The contribution (inaddition to the contribution, also called a contribution rate) mentionedhere is used as a ratio representing a contribution of a change ofspecific contents to an entire change. In the embodiment, thecontributions are contributions to the residual scores Q obtained bycalculating differences between analysis data (for example, arbitraryanalysis data decided as normal data) and other analysis data withrespect to parameters, and are obtained by accumulating normalizedregression coefficients.

For example, of the analysis data of the wafers (126th to 150th wafers)which are decided as abnormal wafers shown in FIG. 3, contributions tothe residual scores Q with respect to the parameters of the analysisdata of the 126th, 140th, and 150th wafers are calculated and shown inFIGS. 4A, 4B, and 4 c. In the bar graphs of the contributions, a waferin which the absolute value of a contribution value is large highlycontributes to a residual score Q. Therefore, according to, e.g., FIG.4A, a parameter having the highest contribution is the parameter C1_U,and a parameter having the second highest contribution is the parameterC2_L.

In this manner, contributions of the parameters of the analysis data arecalculated, a specific parameter which contributes such that theresidual score Q exceeds the predetermined value, i.e., a parameterwhich causes abnormality can be found. In addition, it is understoodthat a parameter having a high contribution causes abnormality with highprobability. For this reason, the parameters can be arranged in thedescending order of probabilities of causing abnormality.

However, the lowest contribution of parameters which cause abnormalitycannot be decided by only the contributions. For example, according toFIG. 4, it is understood that, since the parameters C1_U and C2_L havecertainly high contributions, the parameters cause abnormality with highprobability. However, the parameter Vpp having the next highestcontribution is not decided as a parameter which causes abnormality.

With respect to this point, in order to decide (specify) a parameter asa cause of abnormality, it may be effective that a relation to theresidual score Q is considered because it is sufficient that a cause ofabnormality is removed such that the residual score Q is smaller than apredetermined value.

Therefore, in the present invention, the influences of only parametershaving high contributions in the descending order of contributions areremoved to sequentially calculate the residual scores Q. When theresidual score Q is smaller than a predetermined value, a parameter fromwhich an influence on the residual score Q is removed is decided as aparameter which causes abnormality.

More specifically, the residual scores Q obtained when the contributionsof the parameters are 0 or a value close to 0 are sequentiallycalculated in a descending order of contribution of the parameters. Whenthe residual score Q is lower than the predetermined value, a parameterhaving a contribution which is 0 or a value close to 0 is specified as aparameter which causes abnormality. In this manner, a parameter whichcauses abnormality can be specified.

A method of calculating a residual score Q while the contribution of aparameter is set at 0 or a value close to 0 will be described below. Ingeneral, the parameters of analysis data contribute to the residualscores Q while affecting each other. For this reason, a parameter ismerely replaced with a value obtained when a parameter decided as anormal parameter by the residual score Q, the contribution of theparameter cannot be set at 0 or a value close to 0.

In the example shown in FIG. 3, all the parameters C1_U in the analysisdata of the 126th, 140th, and 150th wafers which are decided as abnormalwafers by the residual scores Q are replaced with the average values ofthe parameters C1_U of the 1st to 125th wafers used as training wafersdecided as normal wafers by the residual scores Q, and the contributionsof the parameters are calculated again. The results are shown in FIG. 5.The obtained results are shown in FIGS. 5A, 5B, and 5C. FIGS. 5A, 5B,and 5C show bar graphs of the contributions of the parameters in the126th, 140th, and 150th wafers. According to FIGS. 5A, 5B, and 5C, forexample, the contribution of the parameter C1_U is kept at a high levelon the minus side and rarely changes. In this manner, when theparameters are merely replaced with values obtained when the parametersare decided as normal parameters by the residual scores Q, only thecontributions of the parameters cannot be set at 0 or a value close to0.

Therefore, according to the present invention, of the parameters ofanalysis data, parameters having contributions to the residual scores Qwhich are desired to be set at 0 or a value close to 0 are predicted bya multivariate analysis, e.g., a PLS method to replace the values, andthe values of the parameters are replaced with the predicted values. Inthis manner, the contributions to the residual scores Q of theparameters can be set at 0 or a value close to 0.

More specifically, the multivariate analysis means 200 sets, of theparameters of analysis data, a specific parameter having a contributionto the residual score Q which is desired to be set at 0 or a value closeto 0 as a variate to be explained (target variate or target variable),and sets the other parameters as explaining variate (explainingvariable). A correlation expression (prediction expression such asregression expression or model) of the following equation (2-1) iscalculated by using a multivariate analysis program.

In the correlation expression given by the following equation (2-1), Xmeans a matrix of explaining variates. The matrix X corresponds toparameters having contributions to the residual scores Q which aredesired to be set at 0 or a value close to 0 are removed from the matrixX in equation (1-1). Reference symbol Y in the regression expressiongiven by the following equation (2-1) means a matrix of variates to beexplained. Reference symbol B means a regression matrix constituted bycoefficients (weights) of the explaining variates, and reference symbolE means a residual matrix.Y=BX+E  (2-1)

In the embodiment, when equation (2-1) is calculated, for example, a PLS(Partial Least Squares) method described in, e.g., JOURNAL OFCHEMOMETRICS, VOL. 2 (PP. 211 to 228) (1998) is used. According to thisPLS method, even though the matrixes X and Y have a large number ofexplaining variates and a large number of variates to be explained, arelational expression between the matrix X and the matrix Y can becalculated by only small number of measured values of the matrixes X andY. Furthermore, according to the characteristic features of the PLSmethod, even though the relational expression is obtained by a smallnumber of measured values, high stability and high reliability can beachieved.

A program for the PLS method is stored in the multivariate analysisprogram storing means 201. The analysis means 212 processes analysisdata according to the procedures of the program to calculate thecorrelation expression (2-1). The obtained result is stored in theanalysis result storing means 205. Therefore, in the embodiment, whenthe correlation expression (2-1) is calculated, a parameter serving asan explaining variate in the analysis data is applied to the matrix X,so that a parameter serving as a target variate in the analysis data canbe predicted. In addition, the predicted value has high reliability.

For example, an ith principal component corresponding to the ithcharacteristic value in an X^(T)Y matrix is expressed by t_(i). When thescore t_(i) of the ith principal component and a vector pi are used, thematrix X is expressed by the following expression (2-2). When the scoret_(i) of the ith principal component and a vector c₁ are used, thematrix Y is expressed by the following equation (2-3). In the followingequations (2-2) and (2-3), X_(i+1) and Y_(i+1) mean residual matrixes ofthe matrixes X and Y, respectively, and X^(T) means a transposed matrixof the matrix X. In the following description, an index T means atransposed matrix.X=t ₁ p ₁ +t ₂ p ₂ +t ₃ p ₃ + . . . +t _(i) p _(i) +X _(i+1)  (2-2)Y=t ₁ c ₁ +t ₂ c ₂ +t ₃ c ₃ + . . . +t _(i) c _(i) +Y _(i+1)  (2-3)

The PLS method used in the first embodiment is a method of calculating aplurality of characteristic numbers and a plurality of characteristicvectors thereof obtained when equation (2-2) and equation (2-3) arecorrelated to each other with a small calculation amount.

The PLS method is performed by the following procedures. On the firststage, centering and scaling operations of the matrixes X and Y areperformed. An equation i=1 is set, and X₁=X and Y₁=Y are satisfied. Thefirst row of the matrix Y₁ is set as u₁. The centering operation is anoperation which subtracts average values of the rows from the values ofthe rows, and the scaling operation is an operation (process) whichdivides the values of the rows by standard deviations of the rows,respectively.

On the second stage, after w_(i)=X_(i) ^(T)u₁/(u_(i) ^(T)u₁) iscalculated, the determinant of w_(i) is normalized to calculatet_(i)=X_(i)w_(i). After the same process as described above is performedto the matrix Y to calculate c_(i)=Y₁ ^(T)t_(i)/(t₁ ^(T)t_(i)), thedeterminant of c_(i) is normalized to calculate u_(i)=Y₁c_(i)/(c₁^(T)c_(i)).

On the third stage, X loading (loading amount) p_(i)=X₁ ^(T)t_(i)/(t_(i)^(T)t_(i)) and Y loading q_(i)=Y₁ ^(T)u_(i)/(u¹u₁) are calculated. Anequation b_(i)=u_(i) ^(T)t₁/ (t_(i) ^(T)t₁) obtained by regressing u tot is calculated. A residual matrix X_(i)=X_(i)−t_(i)p_(i) ^(T) and aresidual matrix Y_(i)=Y_(i)−b_(i)t_(i)c_(i) ^(T) are calculated. Thevalue i is incremented to set i=i+1, and the processes subsequent to theprocess on the second state are repeated. The series of processes arerepeated according to the program of the PLS method until apredetermined stop condition is satisfied or until the residual matrixX_(i+1) is converged to 0, and the maximum characteristic number of theresidual matrix and the characteristic vector of the maximumcharacteristic number are calculated.

In the PLS method, the satisfaction of the stop condition for theresidual matrix X_(i+1) or the convergence to 0 are rapid. When thecalculation is repeated about 10 times, the stop condition of theresidual matrix is satisfied, or the residual matrix converged to 0. Ingeneral, when the calculation is repeated 4 to 5 times, the stopcondition of the residual matrix is satisfied, or the residual matrix isconverged to 0. The maximum characteristic number calculated by thecalculation process and the characteristic vector of the characteristicnumber are used to calculate the first principal component of the X^(T)Ymatrix, and the maximum correlation between the matrix X and the matrixY can be known.

When the correlative expression is obtained by the PLS method, aprediction value of a target variable can be calculated by merelyapplying an explaining variable to the correlative expression. In thismanner, a prediction value of a specific parameter having a contributionto the residual score Q which is desired to be set at 0 or a value closeto 0 can be calculated. When all the values of the specific parametersare replaced with prediction values, the contributions to the residualscores Q can be set at 0 or a value close to 0.

(Experiment Result)

An experiment result obtained when a contribution to a residual score Qis set at 0 or a value close to 0 by replacing parameters of analysisdata with prediction values obtained by the PLS method will be describedbelow. In the example shown in FIG. 3, contributions of parameters inanalysis data of a wafer decided as abnormal wafer by residual scores Qare set at a value close to 0 by using prediction values obtained by thePLS method in a descending order of contribution to the residual scoresQ to calculate new residual scores Q, and degrees of influence on theresidual scores Q are examined.

With respect to the parameter C1_U having the highest parameter in thegraph show in FIG. 4, the contribution is set at a value close to 0.More specifically, the parameter C1_U is set as a target variable, andother parameters are set as explaining variables. A correlativeexpression (2-1) between the parameter C1_U and the other parameters iscalculated by the PLS method. In this case, the 1st to 125th wafersshown in FIG. 3 are used as training wafers, and the correlativeexpression (2-1) is calculated by the analysis data of these wafers.

With respect to the analysis data of the 126th to 150th wafers shown inFIG. 3, parameters except for the parameter C1_U are applied asexplaining variables to the correlative expression (2-1), and predictionvalues of the parameters C1_U of the 126th to 150th wafers arecalculated. Subsequentially, the values of only the parameters C1_U inthe analysis data of the 126th to 150th wafers are replaced with theprediction values calculated as described above, respectively.

When a principal component analysis using the analysis data is performedby using the 1st to 125th wafers as training wafers to calculatecontributions to residual scores Q, graphs shown in FIGS. 6A, 6B, and 6Care obtained. FIGS. 6A, 6B, and 6C show bar graphs of contributionscalculated for the parameters of the 126th, 140th, and 150th wafers ofwafers which are decided as abnormal wafers. According to FIGS. 6A, 6B,and 6C, unlike the case in FIG. 5 in which the values of the parametersC1_U are simply replaced with average values, it is understood that thecontributions of the parameters C1_U are close to 0.

With respect to a parameter C2_L having a high contribution next to thecontribution of the parameter C1_U in the graph shown in FIG. 4, acontribution to a residual score Q is set at a value close to 0. Morespecifically, the parameter C2_L is set as a target variable, and otherparameters are set as explaining variables. A correlative expression(2-1) between the parameter C2_L and the other parameters is calculatedby the PLS method. In this case, the 1st to 125th wafers shown in FIG. 3are used as training wafers, and the correlative expression (2-1) iscalculated by the analysis data of these wafers. As the parameters C1_Uin the explaining variables, the parameters which are changed intoprediction values in the analysis data are directly used. This isbecause the influence of the parameter C2_L is removed while removingthe influence on the residual score Q of the parameter C1_U.

With respect to the analysis data of the 126th to 150th wafers shown inFIG. 3, parameters except for the parameter C2_L are applied asexplaining variables to the correlative expression (2-1), and predictionvalues of the parameters C2_L of the 126th to 150th wafers arecalculated. Subsequentially, the values of only the parameters C2_L inthe analysis data of the 126th to 150th wafers are replaced with theprediction values calculated as described above, respectively like theparameters C1_U.

When a principal component analysis using the analysis data is performedto the 1st to 125th wafers to calculate contributions to residual scoresQ of the 126th, 140th, and 150th wafers which are decided as abnormalwafers, graphs shown in FIGS. 7A, 7B, and 7C are obtained. FIGS. 7A, 7B,and 7C show bar graphs of contributions calculated for the parameters ofthe 126th, 140th, and 150th wafers of the wafers which are decided asabnormal wafers. According to FIGS. 7A, 7B, and 7C, it is understoodthat the contributions of the parameters C1_U and C2_L are close to 0.

(Decision of Parameter Specified as Cause of Abnormality)

A method of deciding a parameter which causes abnormality and has thelowest contribution will be described below. When a parameter is decidedas an abnormal parameter by a residual score Q, influences on theresidual score Q are sequentially removed in a descending order ofcontribution to the residual score Q to calculate a new residual scoreQ. On the basis of the newly calculated residual score Q, a parameterspecified as a cause of abnormality is decided.

More specifically, until the residual score Q is a predetermined valueor less at which a parameter is decided as a normal parameter, thecontributions of parameters are set at 0 or a value close to 0 in adescending order of contribution to the residual score Q. When theresidual score Q is the predetermined value or less at which theparameter is decided as a normal parameter, the parameters havingcontributions which are set at 0 or a value close to 0 until now aredecided as parameters which cause abnormality.

In the example shown in FIG. 3, a case in which a new residual score Qis calculated while removing influences of parameters in a descendingorder of contribution to a residual score will be described below withreference to the drawings. FIG. 8 shows a case in which the contributionof the parameter C1_U is set at a value close to 0 as shown in FIG. 6 tocalculate a new residual score Q. More specifically, the values of onlythe parameters C1_U in the analysis data of the 126th to 150th wafersare replaced with prediction values calculated by the PCA method tocalculate a new residual score Q.

As is apparent from FIG. 8, with respect to the 126th to 150th waferswhich are decided as abnormal wafers, the residual scores Q are smallerthan the residual scores Q shown in FIG. 3. This means that the residualscores Q changes to be better when the influences on the residual scoresQ of the parameters C1_U are removed.

According to FIG. 8, when a predetermined value (decision reference) atwhich the residual score Q is decided as a normal score is 4 or less,the residual scores Q of the 126th to 150th wafers are the predeterminedvalue or less, and the wafers are decided as normal wafers. Therefore,when the decision reference is set, it can be specified that only theparameter C1_U of the analysis data is abnormal. In this case, it issatisfied to perform repair or maintenance based on the parameter C1_U,e.g., an exchange of rectifiers.

In contrast to this, when the predetermined value (decision reference)at which the residual score Q is decided as a normal score is 3 or less,the residual scores Q of the 126th to 150th wafers exceed thepredetermined value, and the wafers are decided as abnormal wafers. Inthis case, a parameter specified as a cause of abnormality is not onlythe parameter C1_U. For this reason, with respect to a parameter havinga high contribution next to the contribution of the parameter C1_U, thecontribution must be set at 0 or a value close to 0 to calculate a newresidual score Q.

Therefore, in the embodiment, since a parameter having a highcontribution next to the contribution of the parameter C1_U is theparameter C2_L (see FIG. 4), with respect to the parameter C2_L, thecontribution is set at 0 or a value close to 0 to calculate a newresidual score Q. FIG. 9 shows a case in which the contribution of theparameter C2_L is set at a value close to 0 as shown in FIG. 7 tocalculate a new residual score Q. More specifically, the values of onlythe parameters C2_L in the analysis data of the 126th to 150th wafersare replaced with prediction values calculated by the PCA method tocalculate a new residual score Q.

As is apparent from FIG. 9, with respect to the 126th to 150th waferswhich are decided as abnormal wafers, the residual scores Q are smallerthan the residual scores Q shown in FIG. 8. This means that the residualscores Q changes to be better when the influences on the residual scoresQ of the parameters C1_U and C2_L are removed.

According to FIG. 9, when a predetermined value (decision reference) atwhich the residual score Q is decided as a normal score is, for example,3 or less, the residual scores Q of the 126th to 150th wafers are thepredetermined value or less, and the wafers are decided as normalwafers. Therefore, when the decision reference is set, it can bespecified that not only parameter C1_U but also the parameter C2_L ofthe analysis data are abnormal. Therefore, in this case, it is satisfiedto perform repair or maintenance based on the parameters C1_U and C2_L,and repair or maintenance based on the other parameters need not beperformed.

In the embodiment, with respect to parameters of analysis data decidedas abnormal data, contributions to, e.g., a residual score Q arecalculated as degrees of influence on the abnormality(degree-of-influence calculation step and degree-of-influencecalculation means), the contributions of the parameters are set at 0 ora value close to 0 in a descending order of contribution of theparameters to sequentially calculate residual scores Q. When theresidual scores Q are a predetermined value or less, the parametershaving contributions which are set at 0 or a value close to 0 aredecided as parameters which cause abnormality (cause-of-abnormalitydeciding step and cause-of-abnormality deciding means). In this manner,since the parameters can be specified depending on the residual scoresQ, parameters which cause abnormality can be appropriately decided. Forthis reason, since repair and maintenance which are necessary to decideanalysis data as normal data can be appropriately performed, the repairand the maintenance can be efficiently performed.

With respect to a parameter from which a degree of influence onabnormality, a correlation between the specific parameter and the otherparameters is calculated by a multivariate analysis, e.g., the PLSmethod. A prediction value of the specific parameter is calculated onthe basis of the correlation, the value of the specific parameter isreplaced with the prediction value. In this manner, the contribution ofthe specific parameter can be easily set at 0 or a value close to 0without forming a new special computing method. However, thecontribution of the specific parameter may be set at 0 or a value closeto 0 by using a special computing method.

The preferred embodiment of the present invention has been describedabove with reference to the accompanying drawings. However, the presentinvention is not limited to the embodiment, as a matter of course. It isapparent to a person skilled in the art that various changes andmodifications can be conceived in the spirit and scope of the presentinvention. It is understood that the various changes and modificationsbelong to the spirit and scope of the invention.

For example, as the plasma processing apparatus, not only aparallel-piped plasma processing apparatus, but also a helicon waveplasma processing apparatus, an inductive coupling plasma processingapparatus, and the like may be used.

As the analysis data, in addition to the data used in the embodiment, VIprobe data, optical data, or trace data may be used. As another tracedata, gas flow rates measured by the mass flow controller 118B, a gaspressure of a back-side gas detected by a pressure meter 115B, a voltageVdc between a high-frequency power supply line (electric wire) and theground as a measured value in the rectifier 107A, and a measured value(traveling wave or the like of a high-frequency power) in the electricmeasuring unit (VI probe) 107C are cited.

The case in which a wafer W is etched has been described above. However,the present invention can also be applied to not only an etching processbut also a processing apparatus which performs a film forming process orthe like. The wafer W is not limited to a wafer to be processed.

As has been described above, according to the present invention, thereis provided a method and apparatus for deciding a cause of abnormalityin a plasma processing apparatus. The method and apparatus for decidinga cause of abnormality can appropriately specify parameters of analysisdata which causes abnormality and can appropriately perform repair andmaintenance which are necessary to decide the analysis data as normaldata.

1. A method of deciding a cause of abnormality in a plasma processingapparatus which performs a plasma processing to an object to beprocessed in a processing chamber, comprising: an analysis dataacquiring step of acquiring analysis data constituted by a plurality ofparameters on the basis of detection values obtained in processes forthe object from a detector arranged in the plasma processing apparatus;an abnormality deciding step of analyzing the acquired analysis data todecide whether the analysis data is abnormal or not; adegree-of-influence calculating step of calculating degrees of influenceon the abnormality of each parameter of the analysis data decided asabnormal data; and a cause-of-abnormality deciding step of decidingwhether the analysis data is abnormal or not after removing theinfluences on the abnormality from the parameter sequentially in thedescending order of the degrees of influence of the parameters, and thendeciding the parameters from which the influences on the abnormality areremoved up to now as parameters which cause the abnormality when it isdecided that the analysis data is normal.
 2. The method of deciding acause of abnormality in a plasma processing apparatus according to claim1, wherein the abnormality decision step has the step of performing aprincipal component analysis to the acquired analysis data to calculatea residual score, and the step of deciding the analysis data having theresidual score which is exceeds a predetermined value as abnormal data,the degree-of-influence calculating step calculates contributions to theresidual score as degrees of influence on abnormality with respect toeach parameter of the analysis data decided as abnormal data, and thecause-of-abnormality deciding step sets contributions of the parametersat 0 or a value close to 0 in a descending order of contribution of theparameters to sequentially calculate residual scores, and, when theresidual scores are not more than the predetermined value, decides theparameters having the contributions which are set at 0 or a value closeto 0 until now as parameters which cause abnormality.
 3. The method ofdeciding a cause of abnormality in a plasma processing apparatusaccording to claim 2, wherein, in the cause-of-abnormality decidingstep, in order to set the contribution of a specific parameter at 0 or avalue close to 0, a correlation between the specific parameter and otherparameters is calculated by a multivariate analysis, a prediction valueof the specific parameter is calculated on the basis of the correlation,and the value of the specific parameter is replaced with the predictionvalue.
 4. The method of deciding a cause of abnormality in a plasmaprocessing apparatus according to claim 3, wherein, the multivariateanalysis is a partial least squares method.
 5. An apparatus for decidinga cause of abnormality in a plasma processing apparatus which performs aplasma processing to an object to be processed in a processing chamber,comprising: analysis data acquiring means for acquiring analysis dataconstituted by a plurality of parameters on the basis of detectionvalues obtained in processes for the object from a detector arranged inthe plasma processing apparatus; abnormality deciding means foranalyzing the acquired analysis data to decide whether the analysis datais abnormal or not; degree-of-influence calculating means forcalculating degrees of influence on the abnormality of each parameter ofthe analysis data decided as abnormal data; and cause-of-abnormalitydeciding means for deciding whether the analysis data is abnormal or notafter removing the influences on the abnormality from the parametersequentially in the descending order of the degrees of influence of theparameters, and then deciding the parameters from which the influenceson the abnormality are removed up to now as parameters which cause theabnormality when it is decided that the analysis data is normal.
 6. Theapparatus of deciding a cause of abnormality in a plasma processingapparatus according to claim 5, wherein the abnormality decision meansperforms a principal component analysis to the acquired analysis data tocalculate a residual score and decides the analysis data having theresidual score which is exceeds a predetermined value as abnormal data,the degree-of-influence calculating means calculates contributions tothe residual score as degrees of influence on abnormality with respectto each parameter of the analysis data decided as abnormal data, and thecause-of-abnormality deciding means sets contributions of the parametersat 0 or a value close to 0 in a descending order of contribution of theparameters to sequentially calculate residual scores, and, when theresidual scores are not more than the predetermined value, decides theparameters having the contributions which are set at 0 or a value closeto 0 until now as parameters which cause abnormality.
 7. The apparatusof deciding a cause of abnormality in a plasma processing apparatusaccording to claim 6, wherein, in the cause-of-abnormality decidingmeans, in order to set the contribution of a specific parameter at 0 ora value close to 0, a correlation between the specific parameter andother parameters is calculated by a multivariate analysis, a predictionvalue of the specific parameter is calculated on the basis of thecorrelation, and the value of the specific parameter is replaced withthe prediction value.
 8. The apparatus of deciding a cause ofabnormality in a plasma processing apparatus according to claim 7,wherein, the multivariate analysis is a partial least squares method.